Digital Approaches to Automated and Machine Learning Assessments of Hearing: Scoping Review.
J Med Internet Res
; 24(2): e32581, 2022 02 02.
Article
in English
| MEDLINE | ID: covidwho-1714906
ABSTRACT
BACKGROUND:
Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals.OBJECTIVE:
This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review.METHODS:
A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report's scope and details was collected to assess the commonalities among the approaches.RESULTS:
A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results.CONCLUSIONS:
In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.Keywords
audiology; automated audiometry; automatic audiometry; automation; digital devices; digital health; digital health technologies; digital hearing; digital hearing health care; hearing loss; machine learning; mobile phone; remote care; self-administered audiometry; self-assessment audiometry; telehealth; user-operated audiometry
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Hearing
/
Hearing Loss
Type of study:
Diagnostic study
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
J Med Internet Res
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
32581
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